Instance Normalization

Introduced by Ulyanov et al. in Instance Normalization: The Missing Ingredient for Fast Stylization

Instance Normalization (also known as contrast normalization) is a normalization layer where:

$$ y_{tijk} = \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 + \epsilon}}, \quad \mu_{ti} = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H x_{tilm}, \quad \sigma_{ti}^2 = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H (x_{tilm} - \mu_{ti})^2. $$

This prevents instance-specific mean and covariance shift simplifying the learning process. Intuitively, the normalization process allows to remove instance-specific contrast information from the content image in a task like image stylization, which simplifies generation.

Source: Instance Normalization: The Missing Ingredient for Fast Stylization

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Translation	105	11.09%
Image-to-Image Translation	89	9.40%
Image Generation	52	5.49%
Style Transfer	48	5.07%
Domain Adaptation	46	4.86%
Semantic Segmentation	42	4.44%
Object Detection	20	2.11%
Unsupervised Domain Adaptation	20	2.11%
Image Segmentation	19	2.01%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

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Normalization